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Title: Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection Abstract: Evasion attacks against machine learning models often succeed via iterative probing of a fixed target model, whereby an attack that succeeds once will succeed repeatedly. One promising approach to counter th... |
Title: A Survey : Neural Networks for AMR-to-Text Abstract: AMR-to-text is one of the key techniques in the NLP community that aims at generating sentences from the Abstract Meaning Representation (AMR) graphs. Since AMR was proposed in 2013, the study on AMR-to-Text has become increasingly prevalent as an essential br... |
Title: Detection of magnetohydrodynamic waves by using machine learning Abstract: Nonlinear wave interactions, such as shock refraction at an inclined density interface, in magnetohydrodynamic (MHD) lead to a plethora of wave patterns with myriad wave types. Identification of different types of MHD waves is an importan... |
Title: On Numerical Integration in Neural Ordinary Differential Equations Abstract: The combination of ordinary differential equations and neural networks, i.e., neural ordinary differential equations (Neural ODE), has been widely studied from various angles. However, deciphering the numerical integration in Neural ODE... |
Title: Cautious Learning of Multiattribute Preferences Abstract: This paper is dedicated to a cautious learning methodology for predicting preferences between alternatives characterized by binary attributes (formally, each alternative is seen as a subset of attributes). By "cautious", we mean that the model learned to ... |
Title: Automatic Detection of Rice Disease in Images of Various Leaf Sizes Abstract: Fast, accurate and affordable rice disease detection method is required to assist rice farmers tackling equipment and expertise shortages problems. In this paper, we focused on the solution using computer vision technique to detect ric... |
Title: Robust SAR ATR on MSTAR with Deep Learning Models trained on Full Synthetic MOCEM data Abstract: The promising potential of Deep Learning for Automatic Target Recognition (ATR) on Synthetic Aperture Radar (SAR) images vanishes when considering the complexity of collecting training datasets measurements. Simulati... |
Title: Modern Machine-Learning Predictive Models for Diagnosing Infectious Diseases Abstract: Controlling infectious diseases is a major health priority because they can spread and infect humans, thus evolving into epidemics or pandemics. Therefore, early detection of infectious diseases is a significant need, and many... |
Title: DiffWire: Inductive Graph Rewiring via the Lov\'asz Bound Abstract: Graph Neural Networks (GNNs) have been shown to achieve competitive results to tackle graph-related tasks, such as node and graph classification, link prediction and node and graph clustering in a variety of domains. Most GNNs use a message pass... |
Title: Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems Abstract: Deep learning methods have been shown to be effective in representing ground-state wave functions of quantum many-body systems. Existing methods use convolutional neural networks (CNNs) for square lattices due to the... |
Title: Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning Abstract: Keeping risk under control is often more crucial than maximizing expected reward in real-world decision-making situations, such as finance, robotics, autonomous driving, etc. The most natural choice of risk measures is varianc... |
Title: Finite-Sample Guarantees for High-Dimensional DML Abstract: Debiased machine learning (DML) offers an attractive way to estimate treatment effects in observational settings, where identification of causal parameters requires a conditional independence or unconfoundedness assumption, since it allows to control fl... |
Title: The Manifold Hypothesis for Gradient-Based Explanations Abstract: When do gradient-based explanation algorithms provide meaningful explanations? We propose a necessary criterion: their feature attributions need to be aligned with the tangent space of the data manifold. To provide evidence for this hypothesis, we... |
Title: Subsurface Depths Structure Maps Reconstruction with Generative Adversarial Networks Abstract: This paper described a method for reconstruction of detailed-resolution depth structure maps, usually obtained after the 3D seismic surveys, using the data from 2D seismic depth maps. The method uses two algorithms bas... |
Title: "Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction Abstract: Dimensionality reduction is a popular preprocessing and a widely used tool in data mining. Transparency, which is usually achieved by means of explanations, is nowadays a widely accepted and crucial requirement of... |
Title: Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers Abstract: Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD\&R) tools that air traffic controllers (ATC... |
Title: Hardening DNNs against Transfer Attacks during Network Compression using Greedy Adversarial Pruning Abstract: The prevalence and success of Deep Neural Network (DNN) applications in recent years have motivated research on DNN compression, such as pruning and quantization. These techniques accelerate model infere... |
Title: Multi-Objective Hyperparameter Optimization -- An Overview Abstract: Hyperparameter optimization constitutes a large part of typical modern machine learning workflows. This arises from the fact that machine learning methods and corresponding preprocessing steps often only yield optimal performance when hyperpara... |
Title: Predicting Gender via Eye Movements Abstract: In this paper, we report the first stable results on gender prediction via eye movements. We use a dataset with images of faces as stimuli and with a large number of 370 participants. Stability has two meanings for us: first that we are able to estimate the standard ... |
Title: Understanding and Optimizing Deep Learning Cold-Start Latency on Edge Devices Abstract: DNNs are ubiquitous on edge devices nowadays. With its increasing importance and use cases, it's not likely to pack all DNNs into device memory and expect that each inference has been warmed up. Therefore, cold inference, the... |
Title: VisageSynTalk: Unseen Speaker Video-to-Speech Synthesis via Speech-Visage Feature Selection Abstract: The goal of this work is to reconstruct speech from a silent talking face video. Recent studies have shown impressive performance on synthesizing speech from silent talking face videos. However, they have not ex... |
Title: A Survey of Detection Methods for Die Attachment and Wire Bonding Defects in Integrated Circuit Manufacturing Abstract: Defect detection plays a vital role in the manufacturing process of integrated circuits (ICs). Die attachment and wire bonding are two steps of the manufacturing process that determine the powe... |
Title: Blind Estimation of a Doubly Selective OFDM Channel: A Deep Learning Algorithm and Theory Abstract: We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems. For systems based on OFDM, we prop... |
Title: Intelligent analysis of EEG signals to assess consumer decisions: A Study on Neuromarketing Abstract: Neuromarketing is an emerging field that combines neuroscience and marketing to understand the factors that influence consumer decisions better. The study proposes a method to understand consumers' positive and ... |
Title: Topological Simplification of Signals for Inference and Approximate Reconstruction Abstract: As Internet of Things (IoT) devices become both cheaper and more powerful, researchers are increasingly finding solutions to their scientific curiosities both financially and computationally feasible. When operating with... |
Title: Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease Abstract: Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of T... |
Title: Deep Multi-Task Networks For Occluded Pedestrian Pose Estimation Abstract: Most of the existing works on pedestrian pose estimation do not consider estimating the pose of an occluded pedestrians, as the annotations of the occluded parts are not available in relevant automotive datasets. For example, CityPersons,... |
Title: Investigating Multi-Feature Selection and Ensembling for Audio Classification Abstract: Deep Learning (DL) algorithms have shown impressive performance in diverse domains. Among them, audio has attracted many researchers over the last couple of decades due to some interesting patterns--particularly in classifica... |
Title: A Deep Learning Network for the Classification of Intracardiac Electrograms in Atrial Tachycardia Abstract: A key technology enabling the success of catheter ablation treatment for atrial tachycardia is activation mapping, which relies on manual local activation time (LAT) annotation of all acquired intracardiac... |
Title: Binary Single-dimensional Convolutional Neural Network for Seizure Prediction Abstract: Nowadays, several deep learning methods are proposed to tackle the challenge of epileptic seizure prediction. However, these methods still cannot be implemented as part of implantable or efficient wearable devices due to thei... |
Title: Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention Abstract: In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify pot... |
Title: Principal Trade-off Analysis Abstract: This paper develops Principal Trade-off Analysis (PTA), a decomposition method, analogous to Principal Component Analysis (PCA), which permits the representation of any game as the weighted sum of disc games (continuous R-P-S games). Applying PTA to empirically generated to... |
Title: QONNX: Representing Arbitrary-Precision Quantized Neural Networks Abstract: We present extensions to the Open Neural Network Exchange (ONNX) intermediate representation format to represent arbitrary-precision quantized neural networks. We first introduce support for low precision quantization in existing ONNX-ba... |
Title: Corruption-Robust Contextual Search through Density Updates Abstract: We study the problem of contextual search in the adversarial noise model. Let $d$ be the dimension of the problem, $T$ be the time horizon and $C$ be the total amount of noise in the system. For the $\eps$-ball loss, we give a tight regret bou... |
Title: BaIT: Barometer for Information Trustworthiness Abstract: This paper presents a new approach to the FNC-1 fake news classification task which involves employing pre-trained encoder models from similar NLP tasks, namely sentence similarity and natural language inference, and two neural network architectures using... |
Title: Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming Abstract: Deep Reinforcement Learning (DRL) is regarded as a potential method for car-following control and has been mostly studied to support a single following vehicle. However, it is more challenging to learn a stab... |
Title: Body Gesture Recognition to Control a Social Robot Abstract: In this work, we propose a gesture based language to allow humans to interact with robots using their body in a natural way. We have created a new gesture detection model using neural networks and a custom dataset of humans performing a set of body ges... |
Title: A Deep Generative Model of Neonatal Cortical Surface Development Abstract: The neonatal cortical surface is known to be affected by preterm birth, and the subsequent changes to cortical organisation have been associated with poorer neurodevelopmental outcomes. Deep Generative models have the potential to lead to... |
Title: MPI: Evaluating and Inducing Personality in Pre-trained Language Models Abstract: Originated as a philosophical quest, personality discerns how individuals differ from each other in terms of thinking, feeling, and behaving. Towards building social machines that work with humans on a daily basis, we are motivated... |
Title: Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation Abstract: Open-Set Domain Adaptation (OSDA) assumes that a target domain contains unknown classes, which are not discovered in a source domain. Existing domain adversarial learning methods are not suitable for OSDA because distribution matc... |
Title: On the fast convergence of minibatch heavy ball momentum Abstract: Simple stochastic momentum methods are widely used in machine learning optimization, but their good practical performance is at odds with an absence of theoretical guarantees of acceleration in the literature. In this work, we aim to close the ga... |
Title: Bayesian Federated Learning via Predictive Distribution Distillation Abstract: For most existing federated learning algorithms, each round consists of minimizing a loss function at each client to learn an optimal model at the client, followed by aggregating these client models at the server. Point estimation of ... |
Title: A Meta-Analysis of Distributionally-Robust Models Abstract: State-of-the-art image classifiers trained on massive datasets (such as ImageNet) have been shown to be vulnerable to a range of both intentional and incidental distribution shifts. On the other hand, several recent classifiers with favorable out-of-dis... |
Title: Contrastive Learning as Goal-Conditioned Reinforcement Learning Abstract: In reinforcement learning (RL), it is easier to solve a task if given a good representation. While deep RL should automatically acquire such good representations, prior work often finds that learning representations in an end-to-end fashio... |
Title: Calibrating Agent-based Models to Microdata with Graph Neural Networks Abstract: Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure the model fulfils its desired purpose. In recent years, simulation-based inference methods have emerged as powerful tools for perform... |
Title: E2V-SDE: From Asynchronous Events to Fast and Continuous Video Reconstruction via Neural Stochastic Differential Equations Abstract: Event cameras respond to brightness changes in the scene asynchronously and independently for every pixel. Due to the properties, these cameras have distinct features: high dynamic... |
Title: A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions Abstract: Clustering is a fundamental machine learning task which has been widely studied in the literature. Classic clustering methods follow the assumption that data are represented as features in a vectorized form through v... |
Title: NatGen: Generative pre-training by "Naturalizing" source code Abstract: Pre-trained Generative Language models (e.g. PLBART, CodeT5, SPT-Code) for source code yielded strong results on several tasks in the past few years, including code generation and translation. These models have adopted varying pre-training o... |
Title: Machine Learning is Abduction Inference Abstract: Concept of Abduction with Gradated Contradictions is introduced here as a form of Peirce's abduction inference. The general form of abduction criterion is formalized in the proposed Logic of Gradated Contradictions and Logic of Recursive Aggregation. Common steps... |
Title: Characteristic kernels on Hilbert spaces, Banach spaces, and on sets of measures Abstract: We present new classes of positive definite kernels on non-standard spaces that are integrally strictly positive definite or characteristic. In particular, we discuss radial kernels on separable Hilbert spaces, and introdu... |
Title: Robust and Sparse Estimation of Linear Regression Coefficients with Heavy-tailed Noises and Covariates Abstract: Robust and sparse estimation of linear regression coefficients is investigated. The situation addressed by the present paper is that covariates and noises are sampled from heavy-tailed distributions, ... |
Title: BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data Abstract: Fast and accurate detection of the disease can significantly help in reducing the strain on the healthcare facility of any country to reduce ... |
Title: Sparse Subspace Clustering in Diverse Multiplex Network Model Abstract: The paper considers the DIverse MultiPLEx (DIMPLE) network model, introduced in Pensky and Wang (2021), where all layers of the network have the same collection of nodes and are equipped with the Stochastic Block Models. In addition, all lay... |
Title: ARES: Locally Adaptive Reconstruction-based Anomaly Scoring Abstract: How can we detect anomalies: that is, samples that significantly differ from a given set of high-dimensional data, such as images or sensor data? This is a practical problem with numerous applications and is also relevant to the goal of making... |
Title: Epistemic Deep Learning Abstract: The belief function approach to uncertainty quantification as proposed in the Demspter-Shafer theory of evidence is established upon the general mathematical models for set-valued observations, called random sets. Set-valued predictions are the most natural representations of un... |
Title: Rethinking Initialization of the Sinkhorn Algorithm Abstract: Computing an optimal transport (OT) coupling between distributions plays an increasingly important role in machine learning. While OT problems can be solved as linear programs, adding an entropic smoothing term is known to result in solvers that are f... |
Title: Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning Abstract: This paper proposes using communication pipelining to enhance the wireless spectrum utilization efficiency and convergence speed of federated learning in mobile edge computing applications... |
Title: Exploring Chemical Space with Score-based Out-of-distribution Generation Abstract: A well-known limitation of existing works on molecule generation is that the generated molecules highly resemble those in the training set. To generate truly novel molecules with completely different structures that may have even ... |
Title: Sublinear Algorithms for Hierarchical Clustering Abstract: Hierarchical clustering over graphs is a fundamental task in data mining and machine learning with applications in domains such as phylogenetics, social network analysis, and information retrieval. Specifically, we consider the recently popularized objec... |
Title: Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays Abstract: The existing analysis of asynchronous stochastic gradient descent (SGD) degrades dramatically when any delay is large, giving the impression that performance depends primarily on the delay. On the contrary, we prove much better guarantees for ... |
Title: Statistical and Computational Phase Transitions in Group Testing Abstract: We study the group testing problem where the goal is to identify a set of k infected individuals carrying a rare disease within a population of size n, based on the outcomes of pooled tests which return positive whenever there is at least... |
Title: Convergence and Price of Anarchy Guarantees of the Softmax Policy Gradient in Markov Potential Games Abstract: We study the performance of policy gradient methods for the subclass of Markov games known as Markov potential games (MPGs), which extends the notion of normal-form potential games to the stateful setti... |
Title: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone Abstract: Vision-language (VL) pre-training has recently received considerable attention. However, most existing end-to-end pre-training approaches either only aim to tackle VL tasks such as image-text retrieval, visual question answering (V... |
Title: Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution Abstract: Outlier detection (OD) literature exhibits numerous algorithms as it applies to diverse domains. However, given a new detection task, it is unclear how to choose an algorithm to use, nor how to set its... |
Title: Classification of ECG based on Hybrid Features using CNNs for Wearable Applications Abstract: Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. Electrocardiography (ECG) is the most widely used screening tool for cardiovascular diseases. Traditionally, ECG signals are cl... |
Title: Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks Abstract: Deep neural networks (DNN) are a promising tool in medical applications. However, the implementation of complex DNNs on battery-powered devices is challenging due to high energy costs for communication. In th... |
Title: Flexible Raman Amplifier Optimization Based on Machine Learning-aided Physical Stimulated Raman Scattering Model Abstract: The problem of Raman amplifier optimization is studied. A differentiable interpolation function is obtained for the Raman gain coefficient using machine learning (ML), which allows for the g... |
Title: Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs Abstract: Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, where... |
Title: Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks Abstract: The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on... |
Title: Classification of EEG Motor Imagery Using Deep Learning for Brain-Computer Interface Systems Abstract: A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if... |
Title: Analysis of Augmentations for Contrastive ECG Representation Learning Abstract: This paper systematically investigates the effectiveness of various augmentations for contrastive self-supervised learning of electrocardiogram (ECG) signals and identifies the best parameters. The baseline of our proposed self-super... |
Title: Experimental Validation of Spectral-Spatial Power Evolution Design Using Raman Amplifiers Abstract: We experimentally validate a machine learning-enabled Raman amplification framework, capable of jointly shaping the signal power evolution in two domains: frequency and fiber distance. The proposed experiment addr... |
Title: Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity Abstract: We propose a general framework to design posterior sampling methods for model-based RL. We show that the proposed algorithms can be analyzed by reducing regret to Hellinger distance based conditional probabil... |
Title: A Unified Sequence Interface for Vision Tasks Abstract: While language tasks are naturally expressed in a single, unified, modeling framework, i.e., generating sequences of tokens, this has not been the case in computer vision. As a result, there is a proliferation of distinct architectures and loss functions fo... |
Title: Wide Bayesian neural networks have a simple weight posterior: theory and accelerated sampling Abstract: We introduce repriorisation, a data-dependent reparameterisation which transforms a Bayesian neural network (BNN) posterior to a distribution whose KL divergence to the BNN prior vanishes as layer widths grow.... |
Title: Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator Abstract: Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast... |
Title: Learning to Accelerate Partial Differential Equations via Latent Global Evolution Abstract: Simulating the time evolution of Partial Differential Equations (PDEs) of large-scale systems is crucial in many scientific and engineering domains such as fluid dynamics, weather forecasting and their inverse optimizatio... |
Title: ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features Abstract: Deep learning models have achieved remarkable success in different areas of machine learning over the past decade; however, the size and complexity of these models make them difficult to understand. I... |
Title: Diffusion Models for Video Prediction and Infilling Abstract: To predict and anticipate future outcomes or reason about missing information in a sequence is a key ability for agents to be able to make intelligent decisions. This requires strong temporally coherent generative capabilities. Diffusion models have s... |
Title: MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields Abstract: Creating fast and accurate force fields is a long-standing challenge in computational chemistry and materials science. Recently, several equivariant message passing neural networks (MPNNs) have been shown ... |
Title: Prefix Language Models are Unified Modal Learners Abstract: With the success of vision-language pre-training, we have witnessed the state-of-the-art has been pushed on multi-modal understanding and generation. However, the current pre-training paradigm is either incapable of targeting all modalities at once (e.g... |
Title: Masked Siamese ConvNets Abstract: Self-supervised learning has shown superior performances over supervised methods on various vision benchmarks. The siamese network, which encourages embeddings to be invariant to distortions, is one of the most successful self-supervised visual representation learning approaches... |
Title: Masked Frequency Modeling for Self-Supervised Visual Pre-Training Abstract: We present Masked Frequency Modeling (MFM), a unified frequency-domain-based approach for self-supervised pre-training of visual models. Instead of randomly inserting mask tokens to the input embeddings in the spatial domain, in this pap... |
Title: Variable Bitrate Neural Fields Abstract: Neural approximations of scalar and vector fields, such as signed distance functions and radiance fields, have emerged as accurate, high-quality representations. State-of-the-art results are obtained by conditioning a neural approximation with a lookup from trainable feat... |
Title: Taxonomy of Benchmarks in Graph Representation Learning Abstract: Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collec... |
Title: Improving Diversity with Adversarially Learned Transformations for Domain Generalization Abstract: To be successful in single source domain generalization, maximizing diversity of synthesized domains has emerged as one of the most effective strategies. Many of the recent successes have come from methods that pre... |
Title: Disparate Impact in Differential Privacy from Gradient Misalignment Abstract: As machine learning becomes more widespread throughout society, aspects including data privacy and fairness must be carefully considered, and are crucial for deployment in highly regulated industries. Unfortunately, the application of ... |
Title: Edge Inference with Fully Differentiable Quantized Mixed Precision Neural Networks Abstract: The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices. Quantizing the parameters and operations to lower bit-precision offers substantial memory and ... |
Title: Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective Abstract: Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the success, oversmoothing has been identified as one of th... |
Title: When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints Abstract: Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance. A centerpiece of a pre... |
Title: Condensing Graphs via One-Step Gradient Matching Abstract: As training deep learning models on large dataset takes a lot of time and resources, it is desired to construct a small synthetic dataset with which we can train deep learning models sufficiently. There are recent works that have explored solutions on co... |
Title: On the Identifiability of Nonlinear ICA: Sparsity and Beyond Abstract: Nonlinear independent component analysis (ICA) aims to recover the underlying independent latent sources from their observable nonlinear mixtures. How to make the nonlinear ICA model identifiable up to certain trivial indeterminacies is a lon... |
Title: Hybrid full-field thermal characterization of additive manufacturing processes using physics-informed neural networks with data Abstract: Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely ph... |
Title: Reconstructing Training Data from Trained Neural Networks Abstract: Understanding to what extent neural networks memorize training data is an intriguing question with practical and theoretical implications. In this paper we show that in some cases a significant fraction of the training data can in fact be recons... |
Title: SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos Abstract: The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision ... |
Title: Pareto Invariant Risk Minimization Abstract: Despite the success of invariant risk minimization (IRM) in tackling the Out-of-Distribution generalization problem, IRM can compromise the optimality when applied in practice. The practical variants of IRM, e.g., IRMv1, have been shown to have significant gaps with I... |
Title: Kantorovich Strikes Back! Wasserstein GANs are not Optimal Transport? Abstract: Wasserstein Generative Adversarial Networks (WGANs) are the popular generative models built on the theory of Optimal Transport (OT) and the Kantorovich duality. Despite the success of WGANs, it is still unclear how well the underlyin... |
Title: HyperImpute: Generalized Iterative Imputation with Automatic Model Selection Abstract: Consider the problem of imputing missing values in a dataset. One the one hand, conventional approaches using iterative imputation benefit from the simplicity and customizability of learning conditional distributions directly,... |
Title: Robust Attack Graph Generation Abstract: We present a method to learn automaton models that are more robust to input modifications. It iteratively aligns sequences to a learned model, modifies the sequences to their aligned versions, and re-learns the model. Automaton learning algorithms are typically very good ... |
Title: A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading Abstract: Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude is lower than the peak pr... |
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